Silhouette-based human pose estimation using reversible jump Markov chain Monte Carlo

S. S. Huang*, L. C. Fu, P. Y. Hsiao

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

1 Scopus citations

Abstract

A novel approach for recovering the human body configuration based on the silhouette is presented. By considering pose inference as traversing the difference subspaces and using a data-driven mechanism, reversible jump Markov chain Monte Carlo (RJMCMC) can explore such solution space very efficiently. Experimental results are provided to demonstrate the efficiency and effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)575-577
Number of pages3
JournalElectronics Letters
Volume42
Issue number10
DOIs
StatePublished - 2006

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